UNIT I:
Introduction – knowledge – based information processing – neutral and fuzzy machine intelligence – fuzziness as multivalance – dynamical system approach to machine intelligence – the brain as a dynamical system – intelligent behaviors as adaptive model free estimation

UNIT II:
Neuoranal dynamics – activation signals – neurons as function – biological activation and signals – neuron fields – neuron dynamical system, common signal function plug – coded signal function. Activation models neuoranal dynamical system – additive neuronal dynamics – auditive neuronal feedback- additive and bivalent model

UNIT III:
Learning : supervised and unsupervised statistical learning – ai learning-neural neteork learning back propagation algorithm and derivation – stopping criteria complexity of learning generalization

UNIT IV:
Fuzzy logic : fuzzy sets and system – universe as a fuzzy sets – geometry of fuzzy sets. Fuzzy and neural function estimation, fuzzy sets . fuzzy and neural function estimation , fuzzy and meta-model controllers – real line target tracking – fuzzy controller – fuzzy and kalman – filter controller surfaces.

Hopfiled networks : The hopfield model – hopfield network algorithm. Bollizman’s machine algorithm – neral network and fuzzy system apllication.

Text Book:
1. BART KOSKO, Neural networks and fuzzy structures

Reference Books:
1. Limin Fr. Neural Networks in computer Intelligence, Mc Graw Hill Publications, Company
2. James A.Freeman, Similarity Neural Networks, Adison Wesley Publications Company.